Biometrics spoofing attack (BsSA) frequently occurs when an adversary impersonates a lawful user to access to biometric system by means of some forged or synthetic samples, especially in fingerprint or face authentication. In allusion to the problem above, the mainstream countermeasure, called biometrics liveness detection (BLD), is raised. In this paper, we propose a more robust and accurate BLD strategy by taking advantage of weighted multimodal convolutional neural networks (MCNN) to extract diverse deep features. Before detection, the ROI operation firstly is performed to remove those invalid backgrounds of fingerprints. Then, a multimodal feature fusion strategy is proposed to make full use of the learning capacity of CNNs without human interactions. It is well known that characteristics of the different direct splicing together and for the subsequent classification is unreasonable, thus, a weighted summation strategy is explored. More specifically, we assign to each type of feature weight contribution rate, sum them, and then learn the optimal combination of different model features. In the final detection phase, to verify our proposed algorithm with higher accuracy, detail analyses of the fingerprint evaluations on intra-database, cross-material, cross-database respectively, which also include assessment under the fusion of different modal features, and face evaluations on same-database, are evaluated. Experimental results on several benchmark datasets LivDet 2011, 2013, 2015 and NUAA demonstrate that our approach achieves outstanding results in fingerprint intra-database and cross-material evaluations as well as face anti-spoofing evaluations comparing with previous methods. Most importantly, our method is more accurate and robust than other existing fingerprint anti-spoofing methods when evaluating on cross-database.